Junk Charts

What to make of the historically low unemployment rate

One of the amazing economic stories of the moment is the unemployment rate, which at around 4% has returned to the level last reached during the peak of the tech boom in 2000. The story is much more complex than it seems.

I devoted a chapter of Numbersense (link) to explain how the government computes unemployment rates. The most important thing to realize is that an unemployment rate of 4 percent does NOT mean that four out of 100 people in the U.S. are unemployed, and 96 out of 100 are employed.

It doesn't even mean that four out of 100 people of working age are unemployed, and 96 out of 100 of working age are employed.

What it means is of the people that the government decides are "employable", 96 out of 100 are employed. Officially, this employability is known as "in labor force." There are many ways to be disqualified from the labor force; one example is if the government decides that the person is not looking for a job.

On the flip side, who the government counts as "employed" also matters! Part-timers are considered employed. They are counted just like a full-time employee in the unemployment metric. Part-time, according to the government, is one to 34 hours worked during the week the survey is administered.

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So two factors can affect the unemployment rate a lot - the proportion of the population considered "not in labor force" (thus not counted at all); and the proportion of those considered employed who are part-timers. (Those are two disjoint groups.)

The following chart then shows that despite the unemployment rate looking great, the U.S. labor market in 2018 looks nothing like what it looked like from 1990 to 2008.

Technical notes: all the data are seasonally adjusted by the Bureau of Labor Statistics. I used a spline to smooth the data first - the top chart shows the smoothed version of the unemployment rates. Smoothing removes month-to-month sharp edges from the second chart. The color scale is based on standardized values of the smoothed data.

 

NYT hits the trifecta with this market correction chart

Yesterday, in the front page of the Business section, the New York Times published a pair of charts that perfectly captures the story of the ongoing turbulence in the stock market.

Here is the first chart:

Most market observers are very concerned about the S&P entering "correction" territory, which the industry arbitrarily defines as a drop of 10% or more from a peak. This corresponds to the shortest line on the above chart.

The chart promotes a longer-term reflection on the recent turbulence, using two reference points: the index has returned to the level even with that at the start of 2018, and about 16 percent higher since the beginning of 2017.

This is all done tastefully in a clear, understandable graphic.

Then, in a bit of a rhetorical flourish, the bottom of the page makes another point:

When viewed back to a 10-year period, this chart shows that the S&P has exploded by 300% since 2009.

A connection is made between the two charts via the color of the lines, plus the simple, effective annotation "Chart above".

The second chart adds even more context, through vertical bands indicating previous corrections (drops of at least 10%). These moments are connected to the first graphic via the beige color. The extra material conveys the message that the market has survived multiple corrections during this long bull period.

Together, the pair of charts addresses a pressing current issue, and presents a direct, insightful answer in a simple, effective visual design, so it hits the Trifecta!

***

There are a couple of interesting challenges related to connecting plots within a multiple-plot framework.

While the beige color connects the concept of "market correction" in the top and bottom charts, it can also be a source of confusion. The orientation and the visual interpretation of those bands differ. The first chart uses one horizontal band while the chart below shows multiple vertical bands. In the first chart, the horizontal band refers to a definition of correction while in the second chart, the vertical bands indicate experienced corrections.

Is there a solution in which the bands have the same orientation and same meaning?

***

These graphs solve a visual problem concerning the visualization of growth over time. Growth rates are anchored to some starting time. A ten-percent reduction means nothing unless you are told ten-percent of what.

Using different starting times as reference points, one gets different values of growth rates. With highly variable series of data like stock prices, picking starting times even a day apart can lead to vastly different growth rates.

The designer here picked several obvious reference times, and superimposes multiple lines on the same plotting canvass. Instead of having four lines on one chart, we have three lines on one, and four lines on the other. This limits the number of messages per chart, which speeds up cognition.

The first chart depicts this visual challenge well. Look at the start of 2018. This second line appears as if you can just reset the start point to 0, and drag the remaining portion of the line down. The part of the top line (to the right of Jan 2018) looks just like the second line that starts at Jan 2018.

However, a closer look reveals that the shape may be the same but the magnitude isn't. There is a subtle re-scaling in addition to the re-set to zero.

The same thing happens at the starting moment of the third line. You can't just drag the portion of the first or second line down - there is also a needed re-scaling.

McKinsey thinks the data world needs more dataviz talent

Note about last week: While not blogging, I delivered four lectures on three topics over five days: one on the use of data analytics in marketing for a marketing class at Temple; two on the interplay of analytics and data visualization, at Yeshiva and a JMP Webinar; and one on how to live during the Data Revolution at NYU.

This week, I'm back at blogging.

McKinsey publishes a report confirming what most of us already know or experience - the explosion of data jobs that just isn't stopping.

On page 5, it says something that is of interest to readers of this blog: "As data grows more complex, distilling it and bringing it to life through visualization is becoming critical to help make the results of data analyses digestible for decision makers. We estimate that demand for visualization grew roughly 50 percent annually from 2010 to 2015." (my bolding)

The report contains a number of unfortunate graphics. Here's one:

I applied my self-sufficiency test by removing the bottom row of data from the chart. Here is what happened to the second circle, representing the fraction of value realized by the U.S. health care industry.

What does the visual say? This is one of the questions in the Trifecta Checkup. We see three categories of things that should add up to 100 percent. With a little more effort, we find the two colored categories are each 10% while the white area is 80%. 

But that's not what the data say, because there is only one thing being measured: how much of the potential has already been realized. The two colors is an attempt to visualize the uncertainty of the estimated proportion, which in this case is described as 10 to 20 percent underneath the chart.

If we have to describe what the two colored sections represent: the dark green section is the lower bound of the estimate while the medium green section is the range of uncertainty. The edge between the two sections is the actual estimated proportion (assuming the uncertainty bound is symmetric around the estimate)!

A first attempt to fix this might be to use line segments instead of colored arcs. 

The middle diagram emphasizes the mid-point estimate while the right diagram, the range of estimates. Observe how differently these two diagrams appear from the original one shown on the left.

This design only works if the reader perceives the chart as a "racetrack" chart. You have to see the invisible vertical line at the top, which is the starting line, and measure how far around the track has the symbol gone. I have previously discussed why I don't like racetracks (for example, here and here).

***

Here is a sketch of another design:

The center figure will have to be moved and changed to a different shape. This design conveys the sense of a goal (at 100%) and how far one is along the path. The uncertainty is represented by wave-like elements that make the exact location of the pointer arrow appear as wavering.

 

 

 

 

Plotted performance guaranteed not to predict future performance

On my flight back from Lyon, I picked up a French magazine, and found the following chart:

A quick visit to Bing Translate tells me that this chart illustrates the rates of return of different types of investments. The headline supposedly says "Only the risk pays". In many investment brochures, after presenting some glaringly optimistic projections of future returns, the vendor legally protects itself by proclaiming "Past performance does not guarantee future performance."

For this chart, an appropriate warning is PLOTTED PERFORMANCE GUARANTEED NOT TO PREDICT THE FUTURE!

***

Two unusual decisions set this chart apart:

1. The tree ring imagery, which codes the data in the widths of concentric rings around a common core

2. The placement of larger numbers toward the middle, and smaller numbers in the periphery.

When a reader takes in the visual design of this chart, what is s/he drawn to?

The designer evidently hopes the reader will focus on comparing the widths of the rings (A), while ignoring the areas or the circumferences. I think it is more likely that the reader will see one of the following:

(B) the relative areas of the tree rings

(C) the areas of the full circles bounded by the circumferences

(D) the lengths of the outer rings

(E) the lengths of the inner rings

(F) the lengths of the "middle" rings (defined as the average of the outer and inner rings)

Here is a visualization of six ways to "see" what is on the French rates of return chart:

Recall the Trifecta Checkup (link). This is an example where "What does the visual say" and "What does the data say" may be at variance. In case (A), if the reader is seeing the ring widths, then those two aspects are in sync. In every other case, the two aspects are disconcordant. 

The level of distortion is visualized in the following chart:

Here, I normalized everything to the size of the SCPI data. The true data is presented by the ring width column, represented by the vertical stripes on the left. If the comparisons are not distorted, the other symbols should stay close to the vertical stripes. One notices there is always distortion in cases (B)-(F). This is primarily due to the placement of the large numbers near the center and the small numbers near the edge. In other words, the radius is inversely proportional to the data!

 The amount of distortion for most cases ranges from 2 to 6 times. 

While the "ring area" (B) version is least distorted on average, it is perhaps the worst of the six representations. The level of distortion is not a regular function of the size of the data. The "sicav monetaries" (smallest data) is the least distorted while the data of medium value are the most distorted.

***

To improve this chart, take a hint from the headline. Someone recognizes that there is a tradeoff between risk and return. The data series shown, which is an annualized return, only paints the return part of the relationship. 

 

 

 

Crazy rich Asians inspire some rich graphics

On the occasion of the hit movie Crazy Rich Asians, the New York Times did a very nice report on Asian immigration in the U.S.

The first two graphics will be of great interest to those who have attended my free dataviz seminar (coming to Lyon, France in October, by the way. Register here.), as it deals with a related issue.

The first chart shows an income gap widening between 1970 and 2016.

This uses a two-lines design in a small-multiples setting. The distance between the two lines is labeled the "income gap". The clear story here is that the income gap is widening over time across the board, but especially rapidly among Asians, and then followed by whites.

The second graphic is a bumps chart (slopegraph) that compares the endpoints of 1970 and 2016, but using an "income ratio" metric, that is to say, the ratio of the 90th-percentile income to the 10th-percentile income.

Asians are still a key story on this chart, as income inequality has ballooned from 6.1 to 10.7. That is where the similarity ends.

Notice how whites now appears at the bottom of the list while blacks shows up as the second "worse" in terms of income inequality. Even though the underlying data are the same, what can be seen in the Bumps chart is hidden in the two-lines design!

In short, the reason is that the scale of the two-lines design is such that the small numbers are squashed. The bottom 10 percent did see an increase in income over time but because those increases pale in comparison to the large incomes, they do not show up.

What else do not show up in the two-lines design? Notice that in 1970, the income ratio for blacks was 9.1, way above other racial groups.

Kudos to the NYT team to realize that the two-lines design provides an incomplete, potentially misleading picture.

***

The third chart in the series is a marvellous scatter plot (with one small snafu, which I'd get t0).

What are all the things one can learn from this chart?

• There is, as expected, a strong correlation between having college degrees and earning higher salaries.
• The Asian immigrant population is diverse, from the perspectives of both education attainment and median household income.
• The largest source countries are China, India and the Philippines, followed by Korea and Vietnam.
• The Indian immigrants are on average professionals with college degrees and high salaries, and form an outlier group among the subgroups.

Through careful design decisions, those points are clearly conveyed.

Here's the snafu. The designer forgot to say which year is being depicted. I suspect it is 2016.

Dating the data is very important here because of the following excerpt from the article:

Asian immigrants make up a less monolithic group than they once did. In 1970, Asian immigrants came mostly from East Asia, but South Asian immigrants are fueling the growth that makes Asian-Americans the fastest-expanding group in the country.

This means that a key driver of the rapid increase in income inequality among Asian-Americans is the shift in composition of the ethnicities. More and more South Asian (most of whom are Indians) arrivals push up the education attainment and household income of the average Asian-American. Not only are Indians becoming more numerous, but they are also richer.

An alternative design is to show two bubbles per ethnicity (one for 1970, one for 2016). To reduce clutter, the smaller ethnicites can be aggregated into Other or South Asian Other. This chart may help explain the driver behind the jump in income inequality.

 

 

 

 

 

Two thousand five hundred ways to say the same thing

Wallethub published a credit card debt study, which includes the following map:

Let's describe what's going on here.

The map plots cities (N = 2,562) in the U.S. Each city is represented by a bubble. The color of the bubble ranges from purple to green, encoding the percentile ranking based on the amount of credit card debt that was paid down by consumers. Purple represents 1st percentile, the lowest amount of paydown while green represents 99th percentile, the highest amount of paydown.

The bubble size is encoding exactly the same data, apparently in a coarser gradation. The more purple the color, the smaller the bubble. The more green the color, the larger the bubble.

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The design decisions are baffling.

Purple is more noticeable than the green, but signifies the less important cities, with the lesser paydowns.

With over 2,500 bubbles crowding onto the map, over-plotting is inevitable. The purple bubbles are printed last, dominating the attention but those are the least important cities (1st percentile). The green bubbles, despite being larger, lie underneath the smaller, purple bubbles.

What might be the message of this chart? Our best guess is: the map explores the regional variation in the paydown rate of credit card debt.

The analyst provides all the data beneath the map. 

From this table, we learn that the ranking is not based on total amount of debt paydown, but the amount of paydown per household in each city (last column). That makes sense.

Shouldn't it be ranked by the paydown rate instead of the per-household number? Divide the "Total Credit Card Paydown by City" by "Total Credit Card Debt Q1 2018" should yield the paydown rate. Surprise! This formula yields a column entirely consisting of 4.16%.

What does this mean? They applied the national paydown rate of 4.16% to every one of 2,562 cities in the country. If they had plotted the paydown rate, every city would attain the same color. To create "variability," they plotted the per-household debt paydown amount. Said differently, the color scale encodes not credit card paydown as asserted but amount of credit card debt per household by city.

Here is a scatter plot of the credit card amount against the paydown amount.

A perfect alignment!

This credit card debt paydown map is an example of a QDV chart, in which there isn't a clear question, there is almost no data, and the visual contains several flaws. (See our Trifecta checkup guide.) We are presented 2,562 ways of saying the same thing: 4.16%.

 

P.S. [6/22/2018] Added scatter plot, and cleaned up some language.

 

 

 

This map steals story-telling from the designer

Stolen drugs is a problem at federal VA hospitals according to the following map.

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Let's evaluate this map from a Trifecta Checkup perspective.

VISUAL - Pass by a whisker. The chosen visual form of a map is standard for geographic data although the map snatches story-telling from our claws, just as people steal drugs from hospitals. Looking at the map, it's not clear what the message is. Is there one?

The 50 states plus DC are placed into five groups based on the reported number of incidents of theft. From the headline, it appears that the journalist conducted a Top 2 Box analysis, defining "significant" losses of drugs as 300 incidents or more. The visual design ignores this definition of "significance."

DATA - Fail. The map tells us where the VA hospitals are located. It doesn't tell us which states are most egregious in drug theft. To learn that, we need to compute a rate, based on the number of hospitals or patients or the amount of spending on drugs.

Looking more carefully, it's not clear they used a Top 2 Box analysis either. I counted seven states with the highest level of theft, followed by another seven states with the second highest level of theft. So the cutoff of twelve states awkwardly lands in between the two levels.

QUESTION - Fail. Drug theft from hospitals is an interesting topic but the graphic does not provide a good answer to the question.

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Even if we don't have data to compute a rate, the chart is a bit better if proportions are emphasized, rather than counts.

 

The proportions are most easily understood from the base of four quarters making the whole. The first group is just over a quarter; the second group is exactly a quarter. The third group plus the first group roughly make up a half. The fourth and fifth groups together almost fills out a quarter.

In the original map, we are told about at least 400 incidents of theft in Texas but given no context to interpret this statistic. What proportion of the total thefts occur in Texas?

 

 

Common charting issues related to connecting lines, labels, sequencing

The following chart about "ranges and trends for digital marketing salaries" has some problems that appear in a great number of charts.

The head tilt required to read the job titles.

The order of the job titles is baffling. It's neither alphabetical nor by salary.

The visual form suggests that we could see trends in salaries reading left-right, but the only information about trends is the year on year salary change, printed on top of the chart.

Some readers will violently object to the connecting lines between job titles, which are discrete categories. In this case, I also agree. I am a fan of so-called profile charts in which we do connect discrete categories with connecting lines - but those charts work because we are comparing the "profiles" of one group versus another group. Here, there is only one group.

The N=3,567 is weird. It doesn't say anything about the reliability of the estimate for say Chief Marketing Officer.

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A dot plot can be used for this dataset. Like this:

The range of salaries is not a great metric as the endpoints could be outliers.

Also, the variability of salaries is affected by two factors: the variability between companies, and sampling variability (which depends on the sample size for each job title). A wide range here could mean that different companies pay different salaries for the same job title, or that very few survey responders held that job title.

 

 

Fantastic visual, but the Google data need some pre-processing

Another entry in the Google Newslab data visualization project that caught my eye is the "How to Fix It" project, illustrating search queries across the world that asks "how." The project web page is here.

The centerpiece of the project is an interactive graphic showing queries related to how to fix home appliances. Here is what it looks like in France (It's always instructive to think about how they would count "France" queries. Is it queries from google.fr? queries written in French? queries from an IP address in France? A combination of the above?)

I particularly appreciate the lack of labels. When we see the pictures, we don't need to be told this is a window and that is a door. The search data concern the relative sizes of the appliances. The red dotted lines show the relative popularity of searches for the respective appliances in aggregate.

By comparison, the Russian picture looks very different:

Are the Russians more sensible? Their searches are far and away about the washing machine, which is the most complicated piece of equipment on the graphic.

At the bottom of the page, the project looks at other queries, such as those related to cooking. I find it fascinating to learn what people need help making:

I have to confess that I searched for "how to make soft boiled eggs". That led me to a lot of different webpages, mostly created for people who search for how to make a soft boiled egg. All of them contain lots of advertising, and the answer boils down to cook it for 6 minutes.

***

The Russia versus France comparison brings out a perplexing problem with the "Data" in this visualization. For competitive reasons, Google does not provide data on search volume. The so-called Search Index is what is being depicted. The Search Index uses the top-ranked item as the reference point (100). In the Russian diagram, the washing machine has Search Index of 100 and everything else pales in comparison.

In the France example, the window is the search item with the greatest number of searches, so it has Search Index of 100; the door has Index 96, which means it has 96% of the search volume of the window; the washing machine with Index 49 has about half the searches of the window.

The numbers cannot be interpreted as proportions. The Index of 49 does not mean that washing machines account for 49% of all France queries about fixing home appliances. That is really the meaning of popularity we want to have but we don't have. We can obtain true popularity measures by "normalizing" the Search Index: just sum up the Index Values of all the appliances and divide the Search Index by the sum of the Indices. After normalizing, the numbers can be interpreted as proportions and they add up to 100% for each country. When not normalized, the indices do not add to 100%.

Take the case in which we have five appliances, and let's say all five appliances are equally popular, comprising 20% of searches each. The five Search Indices will all be 100 because the top-ranked item is given the value of 100. Those indices add to 500!

By contrast, in the case of Russia (or a more extreme case), the top-ranked query is almost 100% of all the searches, so the sum of the indices will be only slightly larger than 100.

If you realize this, then you'd understand that it is risky to compare Search Indices across countries. The interpretation is clouded by how much of the total queries accounted for by the top query.

In our Trifecta Checkup, this is a chart that does well in the Question and Visual corners, but there is a problem with the Data.

 

 

Lines, gridlines, reference lines, regression lines, the works

This post is part 2 of an appreciation of the chart project by Google Newslab, advised by Alberto Cairo, on the gender and racial diversity of the newsroom. Part 1 can be read here.

In the previous discussion, I left out the following scatter bubble plot.

This plot is available in two versions, one for gender and one for race. The key question being asked is whether the leadership in the newsroom is more or less diverse than the rest of the staff.

The story appears to be a happy one: in many newsrooms, the leadership roughly reflects the staff in terms of gender distribution (even though both parts of the whole compare disfavorably to the gender ratio in the neighborhoods, as we saw in the previous post.)

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Unfortunately, there are a few execution problems with this scatter plot.

First, take a look at the vertical axis labels on the right side. The labels inform the leadership axis. The mid-point showing 50-50 (parity) is emphasized with the gray band. Around the mid-point, the labels seem out of place. Typically, when the chart contains gridlines, we expect the labels to sit right around each gridline, either on top or just below the line. Here the labels occupy the middle of the space between successive gridlines. On closer inspection, the labels are correctly affixed, and the gridlines  drawn where they are supposed to be. The designer chose to show irregularly spaced labels: from the midpoint, it's a 15% jump on either side, then a 10% jump.

I find this decision confounding. It also seems as if two people have worked on these labels, as there exists two patterns: the first is "X% Leaders are Women", and second is "Y% Female." (Actually, the top and bottom labels are also inconsistent, one using "women" and the other "female".)

The horizontal axis? They left out the labels. Without labels, it is not possible to interpret the chart. Inspecting several conveniently placed data points, I figured that the labels on the six vertical gridlines should be 25%, 35%, ..., 65%, 75%, in essence the same scale as the vertical axis.

Here is the same chart with improved axis labels:

Re-labeling serves up a new issue. The key reference line on this chart isn't the horizontal parity line: it is the 45-degree line, showing that the leadership has the same proprotion of females as the rest of the staff. In the following plot (right side), I added in the 45-degree line. Note that it is positioned awkwardly on top of the grid system. The culprit is the incompatible gridlines.

 

The solution, as shown below, is to shift the vertical gridlines by 5% so that the 45-degree line bisects every grid cell it touches.

***

Now that we dealt with the purely visual issues, let me get to a statistical issue that's been troubling me. It's about that yellow line. It's supposed to be a regression line that runs through the points.

Does it appear biased downwards to you? It just seems that there are too many dots above and not enough below. The distance of the furthest points above also appears to be larger than that of the distant points below.

How do we know the line is not correct? Notice that the green 45-degree line goes through the point labeled "AVERAGE." That is the "average" newsroom with the average proportion of female staff and the average proportion of leadership staff. Interestingly, the average falls right on the 45-degree line.

In general, the average does not need to hit the 45-degree line. The average, however, does need to hit the regression line! (For a mathematical explanation, see here.)

Note the corresponding chart for racial diversity has it right. The yellow line does pass through the average point here:

 ***

In practice, how do problems seep into dataviz projects? It's the fact that you don't get to the last chart via a clean, streamlined process but that you pass through a cycle of explore-retrench-synthesize, frequently bouncing ideas between several people, and it's challenging to keep consistency!

And let me repeat my original comment about this project - the key learning here is how they took a complex dataset with many variables, broke it down into multiple parts addressing specific problems, and applied the layering principle to make each part of the project digestible.